Sidrah Liaqat is a PhD-level multimedia signal processing researcher and graduate research assistant with 8 years of experience building machine learning systems for audio and video analysis. She specializes in end-to-end deep learning pipelines—custom Transformer training, PyTorch development, and OpenCV-based video processing—applied to behavior recognition and early autism risk detection in collaboration with UC Davis MIND Institute. Her team produced the highest-scoring open-source bird audio detection method in the DCASE 2018 challenge, demonstrating strong domain adaptation and feature-engineering skills. Sidrah pairs rigorous academic training with hands-on deployment experience, from dataset curation to model optimization and evaluation. She also brings proven teaching and mentorship skills from roles in electrical engineering and digital logic instruction. Based in Mountain View, she blends research curiosity with practical engineering to move sensitive clinical and bioacoustic models toward real-world impact.
8 years of coding experience
4 years of employment as a software developer
PhD, Multimedia Signal Processing, Machine Learning, PhD, Multimedia Signal Processing, Machine Learning at University of Kentucky
Master of Science - MS, Electrical Engineering, Master of Science - MS, Electrical Engineering at National University of Sciences and Technology (NUST)
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